ABSTRACT
Fit and accommodation are critical design goals for a body armor system to maximize Soldiers' protection, comfort, mobility, and performance. The aim of this study is to assess fit and accommodation of body armor plates for the US Army. A virtual fit assessment technique, developed, validated, and deployed by NASA for spacesuit design, was adopted for this work. Specifically, 3D manikins of the Soldier population were overlaid virtually with geometrically similar surrogates of the armor plates. Trained subject matter experts with the US Army and NASA manually assessed the fit of the armor plates to manikins using a computer visualization tool and selected the appropriate plate size and position. A prediction model was built from the assessment data to predict the plate size from an arbitrary body shape and the resultant patterns of body-to-plate contact were quantified. The outcome indicated a unique trend of the plate sizes covarying with anthropometry. More pronouncedly, when the overlap between the body tissue and armor plate was quantified, female Soldiers are likely to experience a 25 times larger body-to-plate contact volume and 6.5 times larger contact depth than males on average, due to sex-based anthropometric differences. Overall, the prediction model and contact patterns provided key metrics for virtual body armor fit assessments, of which the locations, patterns, and magnitudes can help to improve sizing and fit of body armor systems, as previously demonstrated for NASA spacesuit design.
ABSTRACT
INTRODUCTION: Personnel engaged in high-stakes occupations, such as military personnel, law enforcement, and emergency first responders, must sustain performance through a range of environmental stressors. To maximize the effectiveness of military personnel, an a priori understanding of traits can help predict their physical and cognitive performance under stress and adversity. This work developed and assessed a suite of measures that have the potential to predict performance during operational scenarios. These measures were designed to characterize four specific trait-based domains: cognitive, health, physical, and social-emotional. MATERIALS AND METHODS: One hundred and ninety-one active duty U.S. Army soldiers completed interleaved questionnaire-based, seated task-based, and physical task-based measures over a period of 3-5 days. Redundancy analysis, dimensionality reduction, and network analyses revealed several patterns of interest. RESULTS: First, unique variable analysis revealed a minimally redundant battery of instruments. Second, principal component analysis showed that metrics tended to cluster together in three to five components within each domain. Finally, analyses of cross-domain associations using network analysis illustrated that cognitive, health, physical, and social-emotional domains showed strong construct solidarity. CONCLUSIONS: The present battery of metrics presents a fieldable toolkit that may be used to predict operational performance that can be clustered into separate components or used independently. It will aid predictive algorithm development aimed to identify critical predictors of individual military personnel and small-unit performance outcomes.
